R

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is the base for many R packages listed in https://cran.r-project.org/

This software is also referenced in ORMS.


References in zbMATH (referenced in 6189 articles , 6 standard articles )

Showing results 1 to 20 of 6189.
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  1. Agostinelli, Claudio; Valdora, Marina; Yohai, Victor J.: Initial robust estimation in generalized linear models (2019)
  2. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  3. Alfaro, Esteban (ed.); Gámez, Matías (ed.); García, Noelia (ed.): Ensemble classification methods with applications in R (2019)
  4. Allévius, Benjamin; Höhle, Michael: An unconditional space-time scan statistic for ZIP-distributed data (2019)
  5. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  6. Anderson, David F.; Higham, Desmond J.; Leite, Saul C.; Williams, Ruth J.: On constrained Langevin equations and (bio)chemical reaction networks (2019)
  7. Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv
  8. Athey, Susan; Tibshirani, Julie; Wager, Stefan: Generalized random forests (2019)
  9. Badih, Ghattas; Pierre, Michel; Laurent, Boyer: Assessing variable importance in clustering: a new method based on unsupervised binary decision trees (2019)
  10. Ben Bond-Lamberty, Kalyn Dorheim, Ryna Cui, Russell Horowitz, Abigail Snyder, Katherine Calvin, Leyang Feng, Rachel Hoesly, Jill Horing, G. Page Kyle, Robert Link, Pralit Patel, Christopher Roney, Aaron Staniszewski, Sean Turner, Min Chen, Felip Feijoo, Corinne Hartin, Mohamad Hejazi, Gokul Iyer, Sonny Kim, Yaling Liu, Cary Lynch, Haewon McJeon, Steven Smith, Stephanie Waldhoff, Marshall Wise, Leon Clarke : Ben Bond-Lamberty , Kalyn Dorheim, Ryna Cui, Russell Horowitz, Abigail Snyder, Katherine Calvin, Leyang Feng, Rachel Hoesly, Jill Horing, G. Page Kyle, Robert Link, Pralit Patel, Christopher Roney, Aaron Staniszewski, Sean Turner, Min Chen, Felip Feijoo, Corinne Hartin, Mohamad Hejazi, Gokul Iyer, Sonny Kim, Yaling Liu, Cary Lynch, Haewon McJeon, Steven Smith, Stephanie Waldhoff, Marshall Wise, Leon Clarke (2019) not zbMATH
  11. Berrar, Daniel; Lopes, Philippe; Dubitzky, Werner: Incorporating domain knowledge in machine learning for soccer outcome prediction (2019)
  12. Blitzstein, Joseph K.; Hwang, Jessica: Introduction to probability (2019)
  13. Blostein, Martin; Miljkovic, Tatjana: On modeling left-truncated loss data using mixtures of distributions (2019)
  14. Bogomolov, Marina; Davidov, Ori: Order restricted univariate and multivariate inference with adjustment for covariates in partially linear models (2019)
  15. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  16. Bücher, Axel; Fermanian, Jean-David; Kojadinovic, Ivan: Combining cumulative sum change-point detection tests for assessing the stationarity of univariate time series (2019)
  17. Cevallos-Valdiviezo, Holger; Van Aelst, Stefan: Fast computation of robust subspace estimators (2019)
  18. Christophe Ambroise, Alia Dehman, Pierre Neuvial, Guillem Rigaill, Nathalie Vialaneix: Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics (2019) arXiv
  19. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  20. Cinzia Franceschini, Nicola Loperfido: MaxSkew and MultiSkew: Two R Packages for Detecting, Measuring and Removing Multivariate Skewness (2019) arXiv

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Further publications can be found at: http://journal.r-project.org/